"""1.2.1节全连接网络MNIST分类实现。修改pytorch/examples，https://github.com/pytorch/examples/blob/main/mnist/main.py.
"""
from __future__ import print_function
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.optim.lr_scheduler import StepLR


class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(784, 128)
        self.fc2 = nn.Linear(128, 128)
        self.fc3 = nn.Linear(128, 10)

    def forward(self, x):
        x = x.reshape(x.shape[0], -1)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        output = F.log_softmax(self.fc3(x))
        return output


def train(args, model, device, train_loader, optimizer, epoch):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        data, target = data.to(device), target.to(device)
        optimizer.zero_grad()
        output = model(data)
        loss = F.nll_loss(output, target)
        loss.backward()
        optimizer.step()
        if batch_idx % args.log_interval == 0:
            print("Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}".format(epoch, batch_idx * len(data), len(train_loader.dataset), 100.0 * batch_idx / len(train_loader), loss.item()))
            if args.dry_run:
                break


def test(model, device, test_loader):
    model.eval()
    test_loss = 0
    correct = 0
    with torch.no_grad():
        for data, target in test_loader:
            data, target = data.to(device), target.to(device)
            output = model(data)
            test_loss += F.nll_loss(output, target, reduction="sum").item()  # sum up batch loss
            pred = output.argmax(dim=1, keepdim=True)  # get the index of the max log-probability
            correct += pred.eq(target.view_as(pred)).sum().item()

    test_loss /= len(test_loader.dataset)

    print("\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n".format(test_loss, correct, len(test_loader.dataset), 100.0 * correct / len(test_loader.dataset)))


def main():
    # Training settings
    parser = argparse.ArgumentParser(description="PyTorch MNIST Example")
    parser.add_argument("--batch-size", type=int, default=64, metavar="N", help="input batch size for training (default: 64)")
    parser.add_argument("--test-batch-size", type=int, default=1000, metavar="N", help="input batch size for testing (default: 1000)")
    parser.add_argument("--epochs", type=int, default=14, metavar="N", help="number of epochs to train (default: 14)")
    parser.add_argument("--lr", type=float, default=1.0, metavar="LR", help="learning rate (default: 1.0)")
    parser.add_argument("--gamma", type=float, default=0.7, metavar="M", help="Learning rate step gamma (default: 0.7)")
    parser.add_argument("--no-cuda", action="store_true", default=False, help="disables CUDA training")
    parser.add_argument("--dry-run", action="store_true", default=False, help="quickly check a single pass")
    parser.add_argument("--seed", type=int, default=1, metavar="S", help="random seed (default: 1)")
    parser.add_argument("--log-interval", type=int, default=10, metavar="N", help="how many batches to wait before logging training status")
    parser.add_argument("--save-model", action="store_true", default=False, help="For Saving the current Model")
    args = parser.parse_args()
    use_cuda = not args.no_cuda and torch.cuda.is_available()

    torch.manual_seed(args.seed)

    device = torch.device("cuda" if use_cuda else "cpu")

    train_kwargs = {"batch_size": args.batch_size}
    test_kwargs = {"batch_size": args.test_batch_size}
    if use_cuda:
        cuda_kwargs = {"num_workers": 1, "pin_memory": True, "shuffle": True}
        train_kwargs.update(cuda_kwargs)
        test_kwargs.update(cuda_kwargs)

    transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
    dataset1 = datasets.MNIST("mnist_data/", train=True, download=True, transform=transform)
    dataset2 = datasets.MNIST("mnist_data/", train=False, transform=transform)
    train_loader = torch.utils.data.DataLoader(dataset1, **train_kwargs)
    test_loader = torch.utils.data.DataLoader(dataset2, **test_kwargs)

    model = Net().to(device)
    optimizer = optim.Adadelta(model.parameters(), lr=args.lr)

    scheduler = StepLR(optimizer, step_size=1, gamma=args.gamma)
    for epoch in range(1, args.epochs + 1):
        train(args, model, device, train_loader, optimizer, epoch)
        test(model, device, test_loader)
        scheduler.step()

    if args.save_model:
        torch.save(model.state_dict(), "mnist_cnn.pt")


if __name__ == "__main__":
    main()
